Abstract

This thesis contains three essays in applied economics which all deal with online markets. The basic idea behind my investigations might seem simple: Identifying valuable information and transforming the obtained advance knowledge into monetary profit, or, at least, into fresh market insights. Online markets provide favorable conditions for implementing automated tasks which would otherwise be highly time-consuming. By automatically recording and analyzing online market events, I am able to acquire valuable information regarding a large number of current interactions with a reasonable amount of effort. The acquired datasets provide a unique opportunity to investigate the market participants' behavior. However, the three chapters of my thesis are not solely concerned with analyzing behavioral data. They are also about recognizing profitable market opportunities that arise, for example, from loopholes in the market's design or from information which should not be public, and acquiring the unique datasets which are the empirical base of the thesis papers. Therefore, Identifying, collecting and evaluating valuable information for this thesis in real-time in order to exploit current bargain opportunities is a challenging and rewarding task. The first chapter addresses the online betting market with regard to the 2012 U.S. Presidential Election. In particular, I recorded minute-by-minute odds from dozens of bookmakers and betting exchanges over a period of 14 months prior to the election. By immediately analyzing this stream of information, I was able to identify price misalignments across the market in real-time. In order to demonstrate that the observed violation of the law of one price actually provides exploitable arbitrage opportunities, I invested real money according to an arbitrage-based strategy. This strategy excludes all potential risks, except for the risk that a platform becomes insolvent. The field experiment highlights the market's accessibility and the costs incurred by implementing the strategy and placing the investments. My methodology therefore enables me to provide evidence that exploitable inter-market arbitrage opportunities exist in this market, affords an insight into the market's dynamics, and also allows me to enjoy a free lunch. Chapter 2 investigates the pay-per-bid auctions provided by labuyla.ch. The disclosure of the auctioned item's hidden price is the main incentive for placing a bid in price reveal auctions. However, I observed that a loophole existed in labuyla.ch's platform which allowed an attentive observer to calculate the hidden price for free. In addition, their auction design allowed me to observe and record the bidding behavior of all the participants on their platform. The analysis of the dataset reveals that this loophole was unknown to the active bidders. Moreover, my investigations on the observed behavior lead to the sole conclusion that the auctioneer himself was cheating by participating in the auctions as seller, bidder and buyer. I confronted the owner of labuyla.ch with my research results and my findings were confirmed by the fact that they rapidly shut down all of their auctions after our discussion. In Chapter 3, I deal with another in-auction fraud, namely: shill bidding. Shill bidding describes the fraudulent behavior of a seller who bids in his own auctions. The anonymity in online auctions and the fact that a single bidder is allowed to open up several accounts on a platform favors such misbehavior. Since auction houses profit from higher sales prices, they have a reduced incentive to prohibit shilling. Several approaches in the literature tackle this problem by identifying shill bidders on the basis of their publicly observable behavior. While inspecting ricardo.ch's website for public behavioral data, I detected an information leakage --- now closed --- that allowed me to observe all accounts' personal details as well as the entered bidders' valuations of the auctioned item. During a four-month period I recorded the bidding history of nearly two million auctions. By comparing the seller's and the bidders' personal details (name, address, and phone number) in these auctions, I was able to accurately identify shill bidders in my dataset and analyze their behavior. In addition, I test the accuracy of two identification algorithms which are based on public information.